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Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2021-02-28 , DOI: 10.1155/2021/6693206
Farong Gao 1 , Taixing Tian 1 , Ting Yao 1 , Qizhong Zhang 1
Affiliation  

Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.

中文翻译:

基于多特征组合和参数优化算法的人体步态识别

准确性是人类步态识别的关键指标。在本文中,我们提出了一种改进的步态识别算法,该算法结合了多特征组合和人工蜂群来优化支持向量机(ABC-SVM)。首先,考虑表面肌电信号(sEMG)的复杂性,从去噪后的sEMG信号中提取出四种类型的特征,包括绝对值(IAV),方差(VAR)和零个数的时域特征。交叉(ZC)点,平均功率频率(MPF)和中值频率(MF)的频域特征,以及小波特征和模糊熵特征。其次,采用SVM,线性判别分析(LDA)和极限学习机(ELM)的分类器来识别具有所获得特征的步态,包括单级特征,多种组合特征以及通过主成分分析(PCA)进行尺寸缩减的优化特征。第三,通过ABC算法对SVM分类器的惩罚系数和核函数参数进行优化,研究了不同特征和分类器对识别结果的影响。最后,训练并识别出用于构造SVM分类器的特征样本。结果表明,ABC-SVM分类器的分类性能明显优于未优化的SVM分类器,平均识别率提高了3.18%。此外,组合特征样本(时域,频域,小波,
更新日期:2021-02-28
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